nep-for New Economics Papers
on Forecasting
Issue of 2021‒07‒19
six papers chosen by
Rob J Hyndman
Monash University

  1. Nowcasting and Short-term Forecasting Turkish GDP: Factor-MIDAS Approach By Selcuk Gul; Abdullah Kazdal
  2. Nowcasting in Tunisia using large datasets and mixed frequency models By Hager Ben Romdhane
  3. Nowcast of Macroeconomic Aggregates in Argentina: Comparing the Predictive Capacity of Different Models By Emilio Blanco; Laura D’Amato; Fiorella Dogliolo; Lorena Garegnani
  4. Time Series Forecasting using a Mixture of Stationary and Nonstationary Predictors By Bodha Hannadige, Sium; Gao, Jiti; Silvapulle, Mervyn; Silvapulle, Param
  5. Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics By Antoine FerrÉ; Guillaume de Certaines; Jérôme Cazelles; Tancrède Cohet; Arash Farnoosh; Frédéric Lantz
  6. Inference and forecasting for continuous-time integer-valued trawl processes and their use in financial economics By Mikkel Bennedsen; Asger Lunde; Neil Shephard; Almut E. D. Veraart

  1. By: Selcuk Gul; Abdullah Kazdal
    Abstract: This paper compares several nowcast approaches that account for mixed-data frequency and “ragged-edge” problems. More specifically, it examines the relative performance of the factor-augmented MIDAS approach (Marcellino and Schumacher; 2010) in nowcasting Turkish GDP with respect to benchmark forecasts. By using 40 monthly indicators in factor extraction, several combinations of the factor-MIDAS models are estimated. Recursive pseudo-out-of sample forecasting exercise in evaluating the alternative models’ performance suggests that factor-augmented MIDAS performs better than the benchmarks, especially in nowcasting. However, they do not provide much information content to forecasting a quarter ahead. Results indicate that taking into account the “ragged-edge” characteristic of the data helps improve the predictive ability of the nowcast models. Besides, dynamic factor extraction methods provide better predictions than the static factor extraction methods.
    Keywords: Forecasting, Mixed frequency, Factor-MIDAS
    JEL: C52 C53 E37
    Date: 2021
  2. By: Hager Ben Romdhane (Central Bank of Tunisia)
    Abstract: The object of this paper is to nowcast, forecast and track changes in Tunisian economic activity during normal and crisis times. The main target variable is quarterly real GDP (RGDP) and we have collected a large and varied set of monthly indicators as predictors. We use several mixed frequency models, such as unrestricted autoregressive MIDAS (UMIDAS-AR), three pass regression filter (3PRF) and mixed dynamic factor models (MDFM). We evaluate these models by comparing them with benchmarking low frequency models including vector autoregressive (VAR) and ARMA models. The dynamic factor and the 3PRF forecasts are more accurate in terms of mean squared errors (MSE) than other alternatives models both in-sample and out of sample in normal times, meaning before the COVID19 period. Forecast errors derived from low frequency models including crisis periods are larger than errors from mixed data sampling approaches including autoregressive terms due mainly to the failure of the low frequency models to capture these tail events. Fortunately, the reliability of nowcasts and forecasts increase when using the mixed frequency dynamic factor model based on information at both monthly and quarterly frequencies.
    Keywords: Mixed Frequency Data Sampling; Nowcasting; short-term forecasting
    JEL: E37 C55 C55 F17 O11
    Date: 2021–06–30
  3. By: Emilio Blanco (Central Bank of Argentina); Laura D’Amato (IIEP UBA); Fiorella Dogliolo (Central Bank of Argentina); Lorena Garegnani (Central Bank of Argentina)
    Abstract: A correct and timely assessment of current macroeconomic conditions is a fundamental input for making monetary policy decisions. Although the main source of macroeconomic data comes from the System of National Accounts - published quarterly and with a significant lag - there is a growing availability of high-frequency economic indicators. In this context, central banks have adopted Nowcasting as a useful tool for more immediate and more precise monitoring of current developments. In this paper, the use of Nowcasting tools is extended to produce forward estimates of two components of domestic aggregate demand: consumption and investment. The exercise uses various sets of indicators, broad and restricted, to construct different dynamic factor models, as well as a combination of forecasts for investment. Finally, the different models are compared in a pseudo-real time exercise and their out of sample performance is evaluated.
    Keywords: dynamic factor models, forecasting, Nowcasting
    JEL: C22 C53 E37
    Date: 2021–01
  4. By: Bodha Hannadige, Sium; Gao, Jiti; Silvapulle, Mervyn; Silvapulle, Param
    Abstract: We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a factor augmented regression [FAR] model. The predictors in the model includes a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables considered to be potential predictors. The novelty of this paper is that it provides a method and justification for a mixture of stationary and nonstationary factors as predictors in the FAR model; we refer to this as mixture-FAR method. This method is important because typically such a large set of panel data, for example the FRED-MD, is likely to contain a mixture of stationary and nonstationary variables. In our simulation study, we observed that the proposed mixture-FAR method performed better than its competitor that requires all the predictors to be nonstationary; the MSE of prediction was at least 33% lower for mixture-FAR. Using the data in FRED-QD for the US, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production. We observed that the mixture-FAR method performed better than its competitors.
    Keywords: Gross domestic product; high dimensional data; industrial production; macroeconomic forecasting; panel data
    JEL: C13 C3 C32 C33
    Date: 2021–01–30
  5. By: Antoine FerrÉ (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Guillaume de Certaines (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Jérôme Cazelles (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Tancrède Cohet (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Frédéric Lantz (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School)
    Abstract: This paper gives an overview of several models applied to forecast the day-ahead prices of the German electricity market between 2014 and 2015 using hourly wind and solar productions as well as load. Four econometric models were built: SARIMA, SARIMAX, Holt-Winters and Monte Carlo Markov Chain Switching Regimes. Two machine learning approaches were also studied: a Gaussian mixture classification coupled with a random forest and finally, an LSTM algorithm. The best performances were obtained using the SARIMAX and LSTM models. The SARIMAX model makes good predictions and has the advantage through its explanatory variables to better capture the price volatility. The addition of other explanatory variables could improve the prediction of the models presented. The RF exhibits good results and allows to build a confidence interval. The LSTM model provides excellent results, but the precise understanding of the functioning of this model is much more complex.
    Keywords: Energy Markets,Renewable Energy,Econometric modelling,Bootstrap Method,Merit-Order effect
    Date: 2021–05
  6. By: Mikkel Bennedsen; Asger Lunde; Neil Shephard; Almut E. D. Veraart
    Abstract: This paper develops likelihood-based methods for estimation, inference, model selection, and forecasting of continuous-time integer-valued trawl processes. The full likelihood of integer-valued trawl processes is, in general, highly intractable, motivating the use of composite likelihood methods, where we consider the pairwise likelihood in lieu of the full likelihood. Maximizing the pairwise likelihood of the data yields an estimator of the parameter vector of the model, and we prove consistency and asymptotic normality of this estimator. The same methods allow us to develop probabilistic forecasting methods, which can be used to construct the predictive distribution of integer-valued time series. In a simulation study, we document good finite sample performance of the likelihood-based estimator and the associated model selection procedure. Lastly, the methods are illustrated in an application to modelling and forecasting financial bid-ask spread data, where we find that it is beneficial to carefully model both the marginal distribution and the autocorrelation structure of the data. We argue that integer-valued trawl processes are especially well-suited in such situations.
    Date: 2021–07

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